paragraph40.75ex plus1ex minus.2ex-1emImage-level self-supervised learning.
Self-supervised learning aims to learn powerful representations by replacing human annotation with proxy tasks derived from data alone.
Current methods can be roughly categorized into instance-level and group-level objectives.
Instance-level objectives include predicting augmentations applied to an image [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}
or learning to discriminate between images [8]}, [9]}, [10]}, [11]}, [12]}, often done by the use of contrastive losses [13]}.
